CITATION.cff

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cff-version: 1.2.0
title: Generate stationary correlated time-series
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Dima
    family-names: Bykhovsky
    orcid: 'https://orcid.org/0000-0003-4468-7791'
  - given-names: Netanel
    family-names: Tochilovsky
  - given-names: Alexander
    family-names: Rudyak
    orcid: 'https://orcid.org/0009-0005-0985-3434'
repository-code: 'https://github.com/bykhov/generate_corr_sequence'
abstract: >-
  This Python function creates a time-series (discrete-time
  random process) with a specific autocorrelation function
  (ACF) and continuous probability distribution, e.g with
  predefined probability density function (PDF).
keywords:
  - Python
  - time-series
  - simulation
  - wss
  - autocorrelation
  - random-process
  - autocovariance
license: MIT
preferred-citation:
  type: conference-paper
  authors:
  - family-names: "Bykhovsky"
    given-names: "Dima"
  - family-names: "Tochilovsky"
    given-names: "Netanel"
  - family-names: "Rudyak"
    given-names: "Alexander"
  doi: "10.1109/ICECET58911.2023.10389427"
  journal: "2023 International Conference on Electrical, Computer and Energy Technologies (ICECET)"
  month: 11
  start: 1 # First page number
  end: 6 # Last page number
  title: "Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function"
  year: 2023